Automated workflow for preparation of cDNA for cap analysis of gene expression on a single molecule sequencer

Applications of deep learning in understanding gene regulation

Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.

CAGE-seq analysis of osteoblast derived from cleidocranial dysplasia human induced pluripotent stem cells

Non-coding RNAs (ncRNAs) comprise a major portion of transcripts and serve an essential role in biological processes. Although the importance of major transcriptomes in osteogenesis has been extensively studied, the function of ncRNAs in human osteogenesis remains unclear. Previously, we developed hiPSCs from patients with cleidocranial dysplasia (CCD) caused by runt-related transcription factor 2 (RUNX2) haploinsufficiency. To gain insight into ncRNAs in osteogenesis, we surveyed differential ncRNA expression profiling and promoter differences of RUNX2 using patient-specific iPSCs and cap analysis gene expression (CAGE) technology to define the promoter landscape. Revertant iPSCs (Rev1 iPSCs) edited by CRISPR/Cas9 system to harbor mutation-corrected RUNX2 exhibited increased proximal promoter expression of RUNX2, while CCD iPSCs did not. We identified 2271 ncRNA genes with altered expression levels before and after differentiation, 31 of which showed at least 20-fold higher expression in Rev1 iPSCs. Bioinformatic analysis also categorized AC007392.3, LINC00379, RP11-122D10.1, and RP11-90J7.2 as enhancer regulatory regions, and HOXA-AS2, MIR219-2, and RP11-834C11.3 as dyadic regulatory regions of these ncRNAs. In addition, two miRNAs, termed MIR199A2 and MIR152, were found to have high enrichment of osteogenic-related terms. Upon further examination of the role of MIR152 on osteoblast differentiation, we found that MIR152 knockdown induced upregulation of ALP and COL1A1 in Saos-2 cells. Thus, ncRNAs were found to regulate the osteogenic differentiation potentials of hiPSCs that are used for bone regeneration and repair owing to their differentiation potentials. These data allow understanding ncRNA profiles of hiPSCs during osteogenesis.

Cap Analysis of Gene Expression (CAGE): A Quantitative and Genome-Wide Assay of Transcription Start Sites

Cap analysis of gene expression (CAGE) is an approach to identify and monitor the activity (transcription initiation frequency) of transcription start sites (TSSs) at single base-pair resolution across the genome. It has been effectively used to identify active promoter and enhancer regions in cancer cells, with potential utility to identify key factors to immunotherapy. Here, we overview a series of CAGE protocols and describe detailed experimental steps of the latest protocol based on the Illumina sequencing platform; both experimental steps (see Subheadings 3.1-3.11) and computational processing steps (see Subheadings 3.12-3.20) are described.

Systematic analysis of transcription start sites in avian development

Cap Analysis of Gene Expression (CAGE) in combination with single-molecule sequencing technology allows precision mapping of transcription start sites (TSSs) and genome-wide capture of promoter activities in differentiated and steady state cell populations. Much less is known about whether TSS profiling can characterize diverse and non-steady state cell populations, such as the approximately 400 transitory and heterogeneous cell types that arise during ontogeny of vertebrate animals. To gain such insight, we used the chick model and performed CAGE-based TSS analysis on embryonic samples covering the full 3-week developmental period. In total, 31,863 robust TSS peaks (>1 tag per million [TPM]) were mapped to the latest chicken genome assembly, of which 34% to 46% were active in any given developmental stage. ZENBU, a web-based, open-source platform, was used for interactive data exploration. TSSs of genes critical for lineage differentiation could be precisely mapped and their activities tracked throughout development, suggesting that non-steady state and heterogeneous cell populations are amenable to CAGE-based transcriptional analysis. Our study also uncovered a large set of extremely stable housekeeping TSSs and many novel stage-specific ones. We furthermore demonstrated that TSS mapping could expedite motif-based promoter analysis for regulatory modules associated with stage-specific and housekeeping genes. Finally, using Brachyury as an example, we provide evidence that precise TSS mapping in combination with Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-on technology enables us, for the first time, to efficiently target endogenous avian genes for transcriptional activation. Taken together, our results represent the first report of genome-wide TSS mapping in birds and the first systematic developmental TSS analysis in any amniote species (birds and mammals). By facilitating promoter-based molecular analysis and genetic manipulation, our work also underscores the value of avian models in unravelling the complex regulatory mechanism of cell lineage specification during amniote development.

FANTOM5 CAGE profiles of human and mouse samples

In the FANTOM5 project, transcription initiation events across the human and mouse genomes were mapped at a single base-pair resolution and their frequencies were monitored by CAGE (Cap Analysis of Gene Expression) coupled with single-molecule sequencing. Approximately three thousands of samples, consisting of a variety of primary cells, tissues, cell lines, and time series samples during cell activation and development, were subjected to a uniform pipeline of CAGE data production. The analysis pipeline started by measuring RNA extracts to assess their quality, and continued to CAGE library production by using a robotic or a manual workflow, single molecule sequencing, and computational processing to generate frequencies of transcription initiation. Resulting data represents the consequence of transcriptional regulation in each analyzed state of mammalian cells. Non-overlapping peaks over the CAGE profiles, approximately 200,000 and 150,000 peaks for the human and mouse genomes, were identified and annotated to provide precise location of known promoters as well as novel ones, and to quantify their activities.

Application of a CAGE Method to an Avian Development Study

Cap analysis of gene expression (CAGE) is a convenient approach for genome-wide identification of promoter regions at single base-pair resolution level and accurate expression estimation of the corresponding transcripts. Depending on the initial biomaterial amount and sequencing technology, different computational pipelines for data processing are available, as well as variations of the CAGE protocol that improve sensitivity and accuracy. Therefore, this chapter elucidates the key steps of sample preparation, sequencing, and data analysis via an example of a promoter expression estimation study in chicken development. We also describe the applicability of this approach for studying other avian and reptilian species.

Paradigm shifts in genomics through the FANTOM projects

Big leaps in science happen when scientists from different backgrounds interact. In the past 15 years, the FANTOM Consortium has brought together scientists from different fields to analyze and interpret genomic data produced with novel technologies, including mouse full-length cDNAs and, more recently, expression profiling at single-nucleotide resolution by cap-analysis gene expression. The FANTOM Consortium has provided the most comprehensive mouse cDNA collection for functional studies and extensive maps of the human and mouse transcriptome comprising promoters, enhancers, as well as the network of their regulatory interactions. More importantly, serendipitous observations of the FANTOM dataset led us to realize that the mammalian genome is pervasively transcribed, even from retrotransposon elements, which were previously considered junk DNA. The majority of products from the mammalian genome are long non-coding RNAs (lncRNAs), including sense-antisense, intergenic, and enhancer RNAs. While the biological function has been elucidated for some lncRNAs, more than 98 % of them remain without a known function. We argue that large-scale studies are urgently needed to address the functional role of lncRNAs.

Batf2/Irf1 induces inflammatory responses in classically activated macrophages, lipopolysaccharides, and mycobacterial infection

Basic leucine zipper transcription factor Batf2 is poorly described, whereas Batf and Batf3 have been shown to play essential roles in dendritic cell, T cell, and B cell development and regulation. Batf2 was drastically induced in IFN-γ-activated classical macrophages (M1) compared with unstimulated or IL-4-activated alternative macrophages (M2). Batf2 knockdown experiments from IFN-γ-activated macrophages and subsequent expression profiling demonstrated important roles for regulation of immune responses, inducing inflammatory and host-protective genes Tnf, Ccl5, and Nos2. Mycobacterium tuberculosis (Beijing strain HN878)-infected macrophages further induced Batf2 and augmented host-protective Batf2-dependent genes, particularly in M1, whose mechanism was suggested to be mediated through both TLR2 and TLR4 by LPS and heat-killed HN878 (HKTB) stimulation experiments. Irf1 binding motif was enriched in the promoters of Batf2-regulated genes. Coimmunoprecipitation study demonstrated Batf2 association with Irf1. Furthermore, Irf1 knockdown showed downregulation of IFN-γ- or LPS/HKTB-activated host-protective genes Tnf, Ccl5, Il12b, and Nos2. Conclusively, Batf2 is an activation marker gene for M1 involved in gene regulation of IFN-γ-activated classical macrophages, as well as LPS/HKTB-induced macrophage stimulation, possibly by Batf2/Irf1 gene induction. Taken together, these results underline the role of Batf2/Irf1 in inducing inflammatory responses in M. tuberculosis infection.

Discovery of molecular markers to discriminate corneal endothelial cells in the human body

The corneal endothelium is a monolayer of hexagonal corneal endothelial cells (CECs) on the inner surface of the cornea. CECs are critical in maintaining corneal transparency through their barrier and pump functions. CECs in vivo have a limited capacity in proliferation, and loss of a significant number of CECs results in corneal edema called bullous keratopathy which can lead to severe visual loss. Corneal transplantation is the most effective method to treat corneal endothelial dysfunction, where it suffers from donor shortage. Therefore, regeneration of CECs from other cell types attracts increasing interests, and specific markers of CECs are crucial to identify actual CECs. However, the currently used markers are far from satisfactory because of their non-specific expression in other cell types. Here, we explored molecular markers to discriminate CECs from other cell types in the human body by integrating the published RNA-seq data of CECs and the FANTOM5 atlas representing diverse range of cell types based on expression patterns. We identified five genes, CLRN1, MRGPRX3, HTR1D, GRIP1 and ZP4 as novel markers of CECs, and the specificities of these genes were successfully confirmed by independent experiments at both the RNA and protein levels. Notably none of them have been documented in the context of CEC function. These markers could be useful for the purification of actual CECs, and also available for the evaluation of the products derived from other cell types. Our results demonstrate an effective approach to identify molecular markers for CECs and open the door for the regeneration of CECs in vitro.

Comparison of CAGE and RNA-seq transcriptome profiling using clonally amplified and single-molecule next-generation sequencing

CAGE (cap analysis gene expression) and RNA-seq are two major technologies used to identify transcript abundances as well as structures. They measure expression by sequencing from either the 5' end of capped molecules (CAGE) or tags randomly distributed along the length of a transcript (RNA-seq). Library protocols for clonally amplified (Illumina, SOLiD, 454 Life Sciences [Roche], Ion Torrent), second-generation sequencing platforms typically employ PCR preamplification prior to clonal amplification, while third-generation, single-molecule sequencers can sequence unamplified libraries. Although these transcriptome profiling platforms have been demonstrated to be individually reproducible, no systematic comparison has been carried out between them. Here we compare CAGE, using both second- and third-generation sequencers, and RNA-seq, using a second-generation sequencer based on a panel of RNA mixtures from two human cell lines to examine power in the discrimination of biological states, detection of differentially expressed genes, linearity of measurements, and quantification reproducibility. We found that the quantified levels of gene expression are largely comparable across platforms and conclude that CAGE and RNA-seq are complementary technologies that can be used to improve incomplete gene models. We also found systematic bias in the second- and third-generation platforms, which is likely due to steps such as linker ligation, cleavage by restriction enzymes, and PCR amplification. This study provides a perspective on the performance of these platforms, which will be a baseline in the design of further experiments to tackle complex transcriptomes uncovered in a wide range of cell types.

Explaining the correlations among properties of mammalian promoters

Proximal promoters are fundamental genomic elements for gene expression. They vary in terms of GC percentage, CpG abundance, presence of TATA signal, evolutionary conservation, chromosomal spread of transcription start sites and breadth of expression across cell types. These properties are correlated, and it has been suggested that there are two classes of promoters: one class with high CpG, widely spread transcription start sites and broad expression, and another with TATA signals, narrow spread and restricted expression. However, it has been unclear why these properties are correlated in this way. We reexamined these features using the deep FANTOM5 CAGE data from hundreds of cell types. First, we point out subtle but important biases in previous definitions of promoters and of expression breadth. Second, we show that most promoters are rather nonspecifically expressed across many cell types. Third, promoters’ expression breadth is independent of maximum expression level, and therefore correlates with average expression level. Fourth, the data show a more complex picture than two classes, with a network of direct and indirect correlations among promoter properties. By tentatively distinguishing the direct from the indirect correlations, we reveal simple explanations for them.

High-throughput transcription profiling identifies putative epigenetic regulators of hematopoiesis

Hematopoietic differentiation is governed by a complex regulatory program controlling the generation of different lineages of blood cells from multipotent hematopoietic stem cells. The transcriptional program that dictates hematopoietic cell fate and differentiation requires an epigenetic memory function provided by a network of epigenetic factors regulating DNA methylation, posttranslational histone modifications, and chromatin structure. Aberrant interactions between epigenetic factors and transcription factors cause perturbations in the blood cell differentiation program that result in various types of hematopoietic disorders. To elucidate the contributions of different epigenetic factors in human hematopoiesis, high-throughput cap analysis of gene expression was used to build transcription profiles of 199 epigenetic factors in a wide range of blood cells. Our epigenetic transcriptome analysis revealed cell type- (eg, HELLS and ACTL6A), lineage- (eg, MLL), and/or leukemia- (eg, CHD2, CBX8, and EPC1) specific expression of several epigenetic factors. In addition, we show that several epigenetic factors use alternative transcription start sites in different cell types. This analysis could serve as a resource for the scientific community for further characterization of the role of these epigenetic factors in blood development.

Simultaneous mapping of transcript ends at single-nucleotide resolution and identification of widespread promoter-associated non-coding RNA governed by TATA elements

Understanding the relationships between regulatory factor binding, chromatin structure, cis-regulatory elements and RNA-regulation mechanisms relies on accurate information about transcription start sites (TSS) and polyadenylation sites (PAS). Although several approaches have identified transcript ends in yeast, limitations of resolution and coverage have remained, and definitive identification of TSS and PAS with single-nucleotide resolution has not yet been achieved. We developed SMORE-seq (simultaneous mapping of RNA ends by sequencing) and used it to simultaneously identify the strongest TSS for 5207 (90%) genes and PAS for 5277 (91%) genes. The new transcript annotations identified by SMORE-seq showed improved distance relationships with TATA-like regulatory elements, nucleosome positions and active RNA polymerase. We found 150 genes whose TSS were downstream of the annotated start codon, and additional analysis of evolutionary conservation and ribosome footprinting suggests that these protein-coding sequences are likely to be mis-annotated. SMORE-seq detected short non-coding RNAs transcribed divergently from more than a thousand promoters in wild-type cells under normal conditions. These divergent non-coding RNAs were less evident at promoters containing canonical TATA boxes, suggesting a model where transcription initiation at promoters by RNAPII is bidirectional, with TATA elements serving to constrain the directionality of initiation.

Capturing drug responses by quantitative promoter activity profiling

Quantitative analysis of cellular responses to drugs is of major interest in pharmaceutical research. Microarray technologies have been widely used for monitoring genome-wide expression changes. However, this approach has several limitations in terms of coverage of targeted RNAs, sensitivity, and quantitativeness, which are crucial for accurate monitoring of cellular responses. In this article, we report an application of genome-wide and quantitative profiling of cellular responses to drugs. We monitored promoter activities in MCF-7 cells by Cap Analysis of Gene Expression using a single-molecule sequencer. We identified a distinct set of promoters affected even by subtle inhibition of the Ras-ERK and phosphatidylinositol-3-kinase-Akt signal-transduction pathways. Furthermore, we succeeded in explaining the majority of promoter responses to inhibition of the upstream epidermal growth factor receptor kinase quantitatively based on the promoter profiles upon inhibition of the two individual downstream signaling pathways. Our results demonstrate unexplored utility of highly quantitative promoter activity profiling in drug research.

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The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). This resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas.